Play Ball!

The visual detection and identification of robots will become more and more important as individual robots will need to interact and in some cases collaborate with other robots while performing different tasks in a common environment. The efficient detection and identification of other robots is a very important issue as most mobile robots have low-processing capabilities and need to manage several different processes in real time (e.g., walking machine). In collaborative scenarios, robots will have to determine behaviors of other robots, in addition to their location. One of the important clues to predicting the future behavior of a robot is to know its line of gaze. In addition, surveillance and/or augmented reality systems operating in scenarios with the presence of robots will also require detecting and identifying them, as well as their behaviors.

[1]  Javier Ruiz-del-Solar,et al.  A Robot Referee for Robot Soccer , 2008, RoboCup.

[2]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[3]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[4]  Javier Ruiz-del-Solar,et al.  Robot Head Pose Detection and Gaze Direction Determination Using Local Invariant Features , 2009, Adv. Robotics.

[5]  Javier Ruiz-del-Solar,et al.  A Fast Probabilistic Model for Hypothesis Rejection in SIFT-Based Object Recognition , 2006, CIARP.

[6]  Javier Ruiz-del-Solar,et al.  A unified learning framework for object detection and classification using nested cascades of boosted classifiers , 2008, Machine Vision and Applications.

[7]  Javier Ruiz-del-Solar,et al.  Detection of AIBO and Humanoid Robots Using Cascades of Boosted Classifiers , 2008, RoboCup.

[8]  Bo Wu,et al.  Fast rotation invariant multi-view face detection based on real Adaboost , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[9]  Cordelia Schmid,et al.  Local Grayvalue Invariants for Image Retrieval , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Tomaso A. Poggio,et al.  Example-Based Learning for View-Based Human Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Antonio Torralba,et al.  Sharing Visual Features for Multiclass and Multiview Object Detection , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Rodrigo Verschae Tannenbaum.  Object Detection Using Nested Cascades of Boosted Classifiers. A Learning Framework and Its Extension to The Multi-Class Case , 2010 .

[14]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[15]  Javier Ruiz-del-Solar,et al.  Multiclass Adaboost and Coupled Classifiers for Object Detection , 2008, CIARP.

[16]  Yuan Li,et al.  High-Performance Rotation Invariant Multiview Face Detection , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Zhi Liu,et al.  Mean shift blob tracking with kernel histogram filtering and hypothesis testing , 2005, Pattern Recognit. Lett..

[18]  Ramakant Nevatia,et al.  Cluster Boosted Tree Classifier for Multi-View, Multi-Pose Object Detection , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[19]  James M. Rehg,et al.  On the Design of Cascades of Boosted Ensembles for Face Detection , 2008, International Journal of Computer Vision.

[20]  Javier Ruiz-del-Solar,et al.  An Automated Refereeing and Analysis Tool for the Four-Legged League , 2006, RoboCup.